AdaCoSeg: Adaptive Shape Co-Segmentation with Group Consistency Loss

Understand

We introduce AdaCoSeg, a deep neural network architecture for adaptive co-segmentation of a set of 3D shapes represented as point clouds.

• Differently from the familiar single-instance segmentation problem, co-segmentation is intrinsically contextual: how a shape is segmented can vary depending on the set it is in.
• Hence, our network features an adaptive learning module to produce a consistent shape segmentation which adapts to a set.
• Specifically, given an input set of unsegmented shapes, we first employ an offline pre-trained part prior network to propose per-shape parts.